Split tests in eager and non-eager mode

Signed-off-by: Yong Tang <[email protected]>

Author: yongtang

tests/test_image.py

@@ -174,29 +174,5 @@ def test_draw_bounding_box(self):
       # self.assertAllEqual(bb_image_v, ex_image_v)
       _ = bb_image_p.eval()
 
-def test_webp_file_dataset():
-  """Test case for WebPDataset.
-  """
-  filename = os.path.join(
-      os.path.dirname(os.path.abspath(__file__)), "test_image", "sample.webp")
-
-  num_repeats = 2
-
-  dataset = image_io.WebPDataset([filename, filename])
-  # Repeat 2 times (2 * 2 = 4 images)
-  dataset = dataset.repeat(num_repeats)
-  # Drop alpha channel
-  dataset = dataset.map(lambda x: x[:, :, :3])
-  # Resize to 224 * 224
-  dataset = dataset.map(lambda x: tf.keras.applications.resnet50.preprocess_input(tf.image.resize(x, (224, 224))))
-  # Batch to 3, still have 4 images (3 + 1)
-  dataset = dataset.batch(1)
-  model = tf.keras.applications.resnet50.ResNet50(weights='imagenet')
-  y = model.predict(dataset)
-  p = tf.keras.applications.resnet50.decode_predictions(y, top=1)
-  for i in p:
-    assert i[0][1] == 'pineapple' # not truly a pineapple, though
-  assert len(p) == 4
-
 if __name__ == "__main__":
   test.main()

tests/test_image_eager.py

@@ -0,0 +1,54 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may not
+# use this file except in compliance with the License.  You may obtain a copy of
+# the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  See the
+# License for the specific language governing permissions and limitations under
+# the License.
+# ==============================================================================
+"""Tests for Image Dataset."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+import tensorflow as tf
+if not (hasattr(tf, "version") and tf.version.VERSION.startswith("2.")):
+  tf.compat.v1.enable_eager_execution()
+import tensorflow_io.image as image_io # pylint: disable=wrong-import-position
+
+
+def test_webp_file_dataset():
+  """Test case for WebPDataset.
+  """
+  filename = os.path.join(
+      os.path.dirname(os.path.abspath(__file__)), "test_image", "sample.webp")
+
+  num_repeats = 2
+
+  dataset = image_io.WebPDataset([filename, filename])
+  # Repeat 2 times (2 * 2 = 4 images)
+  dataset = dataset.repeat(num_repeats)
+  # Drop alpha channel
+  dataset = dataset.map(lambda x: x[:, :, :3])
+  # Resize to 224 * 224
+  dataset = dataset.map(lambda x: tf.keras.applications.resnet50.preprocess_input(tf.image.resize(x, (224, 224))))
+  # Batch to 3, still have 4 images (3 + 1)
+  dataset = dataset.batch(1)
+  model = tf.keras.applications.resnet50.ResNet50(weights='imagenet')
+  y = model.predict(dataset)
+  p = tf.keras.applications.resnet50.decode_predictions(y, top=1)
+  for i in p:
+    assert i[0][1] == 'pineapple' # not truly a pineapple, though
+  assert len(p) == 4
+
+if __name__ == "__main__":
+  test.main()